A Case of Crystalline Keratopathy in Monoclonal Gammopathy of Undetermined Significance (MGUS)

A 62-year-old female visited our clinic with progressively decreased vision in both eyes beginning 12 years prior. Idiopathic corneal opacity in all layers of the cornea was found in both eyes. One year later, we performed penetrating keratoplasty on the undiagnosed right eye. During post-surgical follow-up, corneal edema and stromal opacity recurred, and penetrating keratoplasty was performed two more times. The patient's total serum protein level, which had previously been normal, was elevated prior to the final surgery. She was diagnosed with monoclonal gammopathy of undetermined significance. We made a final diagnosis of monoclonal gammopathy-associated crystalline keratopathy after corneal biopsy. Monoclonal gammopathy-associated crystalline keratopathy is difficult to diagnose and may lead to severe visual loss. A systemic work-up, including serologic tests like serum protein or cholesterol levels, is needed in patients with unexplainable corneal opacity

Unmasking Correlations in Nuclear Cross Sections with Graph Neural Networks

In this work, we explore the use of deep learning techniques to learn the relationships between nuclear cross-sections across the chart of isotopes. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime that are the most important in nuclear science and engineering. We use variational autoencoders (VAEs) and implicit neural representations (INRs) to build a learned feature representation space of nuclear cross sections and reduce the dimensionality of the problem. We then train graph neural networks (GNNs) on the resulting latent space to leverage the topological information encoded in the chart of isotopes and to capture the relationships between cross sections in different nuclei. We find that hypernetworks based on INRs significantly outperforms VAEs in encoding nuclear cross-sections. This superiority is attributed to INR's ability to model complex, varying frequency details, which enables lower prediction errors when combined with GNNs. We also observe that GNN optimization is much more successful when performed in the latent space, whether using INRs or VAEs. However VAEs' continuous representation also allows for direct GNN training in the original input space. We leverage these representational learning techniques and successfully predict cross sections for a 17x17 block of nuclei with high accuracy and precision. These findings suggest that both representation encoding of cross-sections and the prediction task hold significant potential in augmenting nuclear theory models, e.g., providing reliable estimates of covariances of cross sections, including cross-material covariances.Comment: Submitted to Physical Review

Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations

Visualization tools facilitate exploratory data analysis, but fall short at supporting hypothesis-based reasoning. We conducted an exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data. We report on how participants leveraged these unique affordances for visual analysis. We found that a majority of participants articulated meaningful models and predictions, utilizing them as entry points to sensemaking. We contribute an abstract typology representing the types of models participants held and externalized as data expectations. Our findings suggest ways for rearchitecting visual analytics tools to better support hypothesis- and model-based reasoning, in addition to their traditional role in exploratory analysis. We discuss the design implications and reflect on the potential benefits and challenges involved.National Science Foundation award #175561

Hands-on Education Module for Modular Construction, 3D Design, and 4D Schedule

A paradigm shift in teaching modular construction in higher education and K-12 is proposed as a means to increase the future adoption of the modular construction technique. To this effect, a new education module is presented to STEM educators. This education module is based on LEGOs and directed towards educators in the architecture, engineering, and construction (AEC) industry. The main objectives of the education module are to increase interest and knowledge of modular construction, acknowledge the benefits of using 3D design with 4D scheduling, and create a simulating hands-on educational opportunity. The education module is designed to allow participants to experience a hands-on simulation of modular construction and stick-built construction through building a LEGO project. Participants are challenged to find the advantages and disadvantages in both construction systems first-hand and record their findings. Results are presented from the preliminary testing of this education model on a group of construction management students at the University of Nevada, Las Vegas. Overall, the survey results showed that the LEGO education module was successful at achieving the project’s three main objectives: 1) increasing the participants’ interest and knowledge of modular construction through an interactive project; 2) increasing the participants’ understanding of the benefits of 3D design with 4D scheduling over the use of 2D drawings; and 3) creating a simulating hands-on educational opportunity to help participants compare modular construction to stick-built construction. In the end, this proposed a new LEGO education module addressing the problems identified from this study with more participants

Towards Concept-Driven Visual Analytics

Visualizations of data provide a proven method for analysts to explore and make data-driven discoveries. However, current visualization tools provide only limited support for hypothesis-driven analyses, and often lack capabilities that would allow users to visually test the fit of their conceptual models against the data. This imbalance could bias users to overly rely on exploratory visual analysis as the principal mode of inquiry, which can be detrimental to discovery. To address this gap, we propose a new paradigm for 'concept-driven' visual analysis. In this style of analysis, analysts share their conceptual models and hypotheses with the system. The system then uses those inputs to drive the generation of visualizations, while providing plots and interactions to explore places where models and data disagree. We discuss key characteristics and design considerations for concept-driven visualizations, and report preliminary findings from a formative study.National Science Foundation award #175561

SYSTEM AND METHOD FOR FABRICATING POLARIZATION SELECTIVE ELEMENT

A system includes a surface relief grating configured to forwardly diffract an input beam as two linearly polarized beams. The system also includes a waveplate optically coupled with the surface relief grating and configured to convert the two linearly polarized beams into two circularly polarized beams having orthogonal circular polarizations. The two circularly polarized beams having orthogonal circular polarizations interfere with one another to generate a polarization interference pattern

Process optimization for high volumetric productivity with product quality control

High commercial demands of biotherapeutics require high volumetric productivities to accommodate their production with the existing manufacturing infrastructure. While titers are exceeding 5 grams per liter in fed-batch processes, it is imperative that these processes result in consistent and desirable product quality. Here we describe a fed batch process optimization effort resulting in significant increased titer than the initial process. During the optimization, we identified a medium component capable of impacting productivity and two different critical product quality attributes. Through complex screening, the component concentration was shown to be proportional to these product quality modifications in opposing directions, thereby requiring a careful optimization of the delivery range. One of these modifications was recapitulated in a cell free system with media and protein indicating that this was not a result of shift in cellular metabolism unlike the other modification. The mechanism of action and strategies to mitigate this issue were also evaluated. Through this work, a well-controlled process without impacting productivity during large scale manufacturing was designed